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    <titleInfo>
        <title>Semi-Automated Classification of Lake Ice Cover Using Dual Polarization RADARSAT-2 Imagery</title>
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    <name type="personal">
        <namePart type="given">Junqian</namePart>
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        <namePart type="given">Claude</namePart>
        <namePart type="given">R</namePart>
        <namePart type="family">Duguay</namePart>
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        <namePart type="given">David</namePart>
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        <namePart type="family">Clausi</namePart>
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    <name type="personal">
        <namePart type="given">Véronique</namePart>
        <namePart type="family">Pinard</namePart>
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            <title>Remote Sensing, Volume 10, Issue 11</title>
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    <abstract>Lake ice is a significant component of the cryosphere due to its large spatial coverage in high-latitude regions during the winter months. The Laurentian Great Lakes are the world’s largest supply of freshwater and their ice cover has a major impact on regional weather and climate, ship navigation, and public safety. Ice experts at the Canadian Ice Service (CIS) have been manually producing operational Great Lakes image analysis charts based on visual interpretation of the synthetic aperture radar (SAR) images. In that regard, we have investigated the performance of the semi-automated segmentation algorithm “glocal” Iterative Region Growing with Semantics (IRGS) for lake ice classification using dual polarized RADARSAT-2 imagery acquired over Lake Erie. Analysis of various case studies indicated that the “glocal” IRGS algorithm could provide a reliable ice-water classification using dual polarized images with a high overall accuracy of 90.4%. However, lake ice types that are based on stage of development were not effectively identified due to the ambiguous relation between backscatter and ice types. The slight improvement of using dual-pol as opposed to single-pol images for ice-water discrimination was also demonstrated.</abstract>
    <identifier type="citekey">Wang-2018-Semi-Automated</identifier>
    <identifier type="doi">10.3390/rs10111727</identifier>
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        <url>https://gwf-uwaterloo.github.io/gwf-publications/G18-123001</url>
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    <part>
        <date>2018</date>
        <detail type="volume"><number>10</number></detail>
        <detail type="issue"><number>11</number></detail>
        <detail type="page"><number>1727</number></detail>
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%0 Journal Article
%T Semi-Automated Classification of Lake Ice Cover Using Dual Polarization RADARSAT-2 Imagery
%A Wang, Junqian
%A Duguay, Claude R.
%A Clausi, David A.
%A Pinard, Véronique
%A Howell, Stephen
%J Remote Sensing, Volume 10, Issue 11
%D 2018
%V 10
%N 11
%I MDPI AG
%F Wang-2018-Semi-Automated
%X Lake ice is a significant component of the cryosphere due to its large spatial coverage in high-latitude regions during the winter months. The Laurentian Great Lakes are the world’s largest supply of freshwater and their ice cover has a major impact on regional weather and climate, ship navigation, and public safety. Ice experts at the Canadian Ice Service (CIS) have been manually producing operational Great Lakes image analysis charts based on visual interpretation of the synthetic aperture radar (SAR) images. In that regard, we have investigated the performance of the semi-automated segmentation algorithm “glocal” Iterative Region Growing with Semantics (IRGS) for lake ice classification using dual polarized RADARSAT-2 imagery acquired over Lake Erie. Analysis of various case studies indicated that the “glocal” IRGS algorithm could provide a reliable ice-water classification using dual polarized images with a high overall accuracy of 90.4%. However, lake ice types that are based on stage of development were not effectively identified due to the ambiguous relation between backscatter and ice types. The slight improvement of using dual-pol as opposed to single-pol images for ice-water discrimination was also demonstrated.
%R 10.3390/rs10111727
%U https://gwf-uwaterloo.github.io/gwf-publications/G18-123001
%U https://doi.org/10.3390/rs10111727
%P 1727

Markdown (Informal)

[Semi-Automated Classification of Lake Ice Cover Using Dual Polarization RADARSAT-2 Imagery](https://gwf-uwaterloo.github.io/gwf-publications/G18-123001) (Wang et al., GWF 2018)

• Semi-Automated Classification of Lake Ice Cover Using Dual Polarization RADARSAT-2 Imagery (Wang et al., GWF 2018)

ACL

• Junqian Wang, Claude R. Duguay, David A. Clausi, Véronique Pinard, and Stephen Howell. 2018. Semi-Automated Classification of Lake Ice Cover Using Dual Polarization RADARSAT-2 Imagery. Remote Sensing, Volume 10, Issue 11, 10(11):1727.